Static and moving object recognition is a standard task in many industrial processes such as automation technology, conveyor belts, robots, etc., and consumer applications such as household appliances, electromechanical toys, etc. In its simplest form, object recognition is typically realized by a photosensor (light barrier) which can detect whether an object is present or not, but which cannot easily distinguish between objects having different shapes, moving with different velocities, etc. In a more sophisticated form, object recognition is typically realized by a camera system in combination with digital image processing for pattern recognition. Such systems provide a much wider range of functions including differentiation between different object shapes, velocities, etc., but are also much more complex and expensive.
Material analysis systems that include sensors for detecting the level of impurity in liquids and gases are gaining importance. Low-end material analysis systems are typically realized by a photoelectric sensor which can measure light intensity drop due to light absorbing impurities (e.g. particles) of a liquid or a gas in the space between the light source and sensor element. Smoke detectors are one example. Such devices are capable of detecting the total amount of impurity, but cannot provide information about particle size, particle homogeneity, flow velocity of particles in the gas or the liquid, etc. High-end material analysis systems are typically realized by a laser-based particle sensor which employs a light scattering method and can provide more advanced functionality, but are more complex and expensive.
Here, there is a need for compact, robust, easy-to-use and inexpensive object recognition and material analysis systems with extended functionality which bridges the gap between existing low-end and high-end object solutions.